In general, noise filtering is important in a number of signal processing areas including electrocardiology, electroencephalography, sound recording, data transmission and communications. By providing the appropriate noise filtering, a particular signal can be utilized in a manner such that the undesirable effects of noise are substantially eliminated.
By way of example, recorded components of an electrocardiographic (ECG) signal (P-waves, for example) are frequently difficult to observe unambiguously due to effects such as noise, superposition on other ECG signals (T-waves, for example), or unusually low amplitudes. Improving the signal-to-noise ratio of an ECG signal allows the characteristics of a P-wave, which may be masked by noise, to be better detected and identified. Also, electrocardiographic micro-potentials, which usually require signal averaging to detect, may be further enhanced by using the appropriate filtering to compare multiple averages.
Another problem encountered when recording the components of an ECG signal is the effect of noise on QRS detection. More specifically, arrhythmia monitoring is plagued by false-positive QRS detection caused by noise artifacts of non-cardiac origin. For example, the source of such noise artifacts may be from skeletal muscle action. In addition, arrhythmia monitoring is dependent upon the ability to accurately determine the characteristics of P-waves. Accurate detection of P-waves is central to correct arrhythmia classification.
Thus, while various arrangements are available for filtering ECG signals, there remains a need for improving the signal-to-noise ratio of ECG signals to permit the accurate determination of the characteristics of ECG signals such as P-waves. There also remains the need for filtering which can also be used for wave delimiting or wave onset/offset detection, reducing noise while maintaining diagnostic bandwidth, and improving data compression efficiency for transmission and/or signal storage. In addition, there remains the need for filtering which provides differentiation of signal origin to parts of the heart, electrode misplacement detection, discrimination of true ECG change from electrode position effects, recreation of missing leads, event detection of power parameters, and non-cardiac signal detection, such as fetal ECG.